# Class 05: Data Visualization
# Today we are going to use ggplot2 package
# First we need to load the package!
# install.packages("ggplot2")
library(ggplot2)
# We will use this inbuilt "cars" dataset first
head(cars)
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
# All ggplots have at least 3 layers,
# data + aes + geoms
ggplot(data=cars) +
aes(x=speed, y=dist) +
geom_point() +
geom_smooth(method="lm") +
labs(title="Stopping Distance of Old Cars",
x="Speed (MPH)",
y="Stopping Distance (ft)")
## `geom_smooth()` using formula 'y ~ x'

# Side note: ggplot is not the only graphics system
# A very popular one is good old "base" R graphics
plot(cars)

url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)
head(genes)
## Gene Condition1 Condition2 State
## 1 A4GNT -3.6808610 -3.4401355 unchanging
## 2 AAAS 4.5479580 4.3864126 unchanging
## 3 AASDH 3.7190695 3.4787276 unchanging
## 4 AATF 5.0784720 5.0151916 unchanging
## 5 AATK 0.4711421 0.5598642 unchanging
## 6 AB015752.4 -3.6808610 -3.5921390 unchanging
#Q How many genes in this dataset
nrow(genes)
## [1] 5196
colnames(genes)
## [1] "Gene" "Condition1" "Condition2" "State"
ncol(genes)
## [1] 4
#Q How many genes are up
table(genes$State)
##
## down unchanging up
## 72 4997 127
# To obtain the % of up genes compared to total genes:
round( table(genes$State)/nrow(genes) * 100, 2 )
##
## down unchanging up
## 1.39 96.17 2.44
# Make first basic scatter plot
ggplot(data=genes) +
aes(x=Condition1, y=Condition2) +
geom_point()

# Adding a third object, State (genes up or down) and saving it as an object, "p":
p <- ggplot(genes) +
aes(x=Condition1, y=Condition2, col=State) +
geom_point()
p

# Changing colors:
p + scale_colour_manual( values=c("blue", "gray", "red") )

# Changing labels:
p + scale_colour_manual( values=c("blue", "gray", "red") ) +
labs(title="Gene Expression Changes Upon Drug Treatment",
x="Control (no drug)",
y="Drug Treatment")

# Let's explore the gapminder dataset
# install.packages("gapminder")
library(gapminder)
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
# Let's make a new plot of year vs lifeExp (we can use boxplot/violin)
ggplot(gapminder) +
aes(x=year, y=lifeExp, color=continent) +
geom_jitter(width=0.3,alpha=0.4) +
geom_violin( aes(group=year), alpha=0.2, draw_quantiles = c(0.5))

# Let's turn it interactive
#Install the plotly package
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplotly()
ggplotly(p)